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Title: Representing First-Order Causal Theories by Logic Programs |
Abstract: Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner, became a basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about acti... |
Title: Doubly Robust Policy Evaluation and Learning |
Abstract: We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central ta... |
Title: Theoretical Properties of the Overlapping Groups Lasso |
Abstract: We present two sets of theoretical results on the grouped lasso with overlap of Jacob, Obozinski and Vert (2009) in the linear regression setting. This method allows for joint selection of predictors in sparse regression, allowing for complex structured sparsity over the predictors encoded as a set of groups.... |
Title: Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models |
Abstract: Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to... |
Title: Automatic Extraction of Open Space Area from High Resolution Urban Satellite Imagery |
Abstract: In the 21st century, Aerial and satellite images are information rich. They are also complex to analyze. For GIS systems, many features require fast and reliable extraction of open space area from high resolution satellite imagery. In this paper we will study efficient and reliable automatic extraction algori... |
Title: A comparison of Gap statistic definitions with and without logarithm function |
Abstract: The Gap statistic is a standard method for determining the number of clusters in a set of data. The Gap statistic standardizes the graph of $\log(W_k)$, where $W_k$ is the within-cluster dispersion, by comparing it to its expectation under an appropriate null reference distribution of the data. We suggest to ... |
Title: Formal and Computational Properties of the Confidence Boost of Association Rules |
Abstract: Some existing notions of redundancy among association rules allow for a logical-style characterization and lead to irredundant bases of absolutely minimum size. One can push the intuition of redundancy further and find an intuitive notion of interest of an association rule, in terms of its "novelty" with resp... |
Title: The Discrete Infinite Logistic Normal Distribution |
Abstract: We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representati... |
Title: ASN-Minimax double sampling plans by variables for two-sided specification limits when \sigma is unknown |
Abstract: ASN-Minimax double sampling plans by variables for a normally distributed quality characteristic with unknown standard deviation and two-sided specification limits are introduced. These plans base on the essentially Maximum-Likelihood (ML) estimator p* and the Minimum Variance Unbiased (MVU) estimator ^p of t... |
Title: When is social computation better than the sum of its parts? |
Abstract: Social computation, whether in the form of searches performed by swarms of agents or collective predictions of markets, often supplies remarkably good solutions to complex problems. In many examples, individuals trying to solve a problem locally can aggregate their information and work together to arrive at a... |
Title: Cooperative searching for stochastic targets |
Abstract: Spatial search problems abound in the real world, from locating hidden nuclear or chemical sources to finding skiers after an avalanche. We exemplify the formalism and solution for spatial searches involving two agents that may or may not choose to share information during a search. For certain classes of tas... |
Title: An Information-Theoretic Approach to Nonparametric Estimation, Model Selection, and Goodness of Fit |
Abstract: This paper applies the recently axiomatized Optimum Information Principle (minimize the Kullback-Leibler information subject to all relevant information) to nonparametric density estimation, which provides a theoretical foundation as well as a computational algorithm for maximum entropy density estimation. Th... |
Title: Dynamic Markov Bases |
Abstract: We present a computational approach for generating Markov bases for multi-way contingency tables whose cells counts might be constrained by fixed marginals and by lower and upper bounds. Our framework includes tables with structural zeros as a particular case. In- stead of computing the entire Markov basis in... |
Title: Classification of Sets using Restricted Boltzmann Machines |
Abstract: We consider the problem of classification when inputs correspond to sets of vectors. This setting occurs in many problems such as the classification of pieces of mail containing several pages, of web sites with several sections or of images that have been pre-segmented into smaller regions. We propose general... |
Title: Distribution-Independent Evolvability of Linear Threshold Functions |
Abstract: Valiant's (2007) model of evolvability models the evolutionary process of acquiring useful functionality as a restricted form of learning from random examples. Linear threshold functions and their various subclasses, such as conjunctions and decision lists, play a fundamental role in learning theory and hence... |
Title: Automatic Open Space Area Extraction and Change Detection from High Resolution Urban Satellite Images |
Abstract: In this paper, we study efficient and reliable automatic extraction algorithm to find out the open space area from the high resolution urban satellite imagery, and to detect changes from the extracted open space area during the period 2003, 2006 and 2008. This automatic extraction and change detection algorit... |
Title: Sufficient Component Analysis for Supervised Dimension Reduction |
Abstract: The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we propose a novel distribution-free SDR method called sufficient component analysis (SCA), which is computationally more efficient than... |
Title: User Modeling Combining Access Logs, Page Content and Semantics |
Abstract: The paper proposes an approach to modeling users of large Web sites based on combining different data sources: access logs and content of the accessed pages are combined with semantic information about the Web pages, the users and the accesses of the users to the Web site. The assumption is that we are dealin... |
Title: On Understanding and Machine Understanding |
Abstract: In the present paper, we try to propose a self-similar network theory for the basic understanding. By extending the natural languages to a kind of so called idealy sufficient language, we can proceed a few steps to the investigation of the language searching and the language understanding of AI. Image underst... |
Title: An Empirical Study of Real-World SPARQL Queries |
Abstract: Understanding how users tailor their SPARQL queries is crucial when designing query evaluation engines or fine-tuning RDF stores with performance in mind. In this paper we analyze 3 million real-world SPARQL queries extracted from logs of the DBPedia and SWDF public endpoints. We aim at finding which are the ... |
Title: Mining User Comment Activity for Detecting Forum Spammers in YouTube |
Abstract: Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automati... |
Title: From Linked Data to Relevant Data -- Time is the Essence |
Abstract: The Semantic Web initiative puts emphasis not primarily on putting data on the Web, but rather on creating links in a way that both humans and machines can explore the Web of data. When such users access the Web, they leave a trail as Web servers maintain a history of requests. Web usage mining approaches hav... |
Title: Algorithms for computing the greatest simulations and bisimulations between fuzzy automata |
Abstract: Recently, two types of simulations (forward and backward simulations) and four types of bisimulations (forward, backward, forward-backward, and backward-forward bisimulations) between fuzzy automata have been introduced. If there is at least one simulation/bisimulation of some of these types between the given... |
Title: Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics |
Abstract: Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Though early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. ... |
Title: Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization |
Abstract: We theoretically investigate the convergence rate and support consistency (i.e., correctly identifying the subset of non-zero coefficients in the large sample limit) of multiple kernel learning (MKL). We focus on MKL with block-l1 regularization (inducing sparse kernel combination), block-l2 regularization (i... |
Title: Fast Learning Rate of lp-MKL and its Minimax Optimality |
Abstract: In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instead of l1-mixed-norm regularization. We utilize localization techniques to obtain the sharp learning rate. The bound is characterize... |
Title: Interpretable Clustering using Unsupervised Binary Trees |
Abstract: We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), considerat... |
Title: Parameter Estimation for Hidden Markov Models with Intractable Likelihoods |
Abstract: Approximate Bayesian computation (ABC) is a popular technique for approximating likelihoods and is often used in parameter estimation when the likelihood functions are analytically intractable. Although the use of ABC is widespread in many fields, there has been little investigation of the theoretical propert... |
Title: Data augmentation for non-Gaussian regression models using variance-mean mixtures |
Abstract: We use the theory of normal variance-mean mixtures to derive a data-augmentation scheme for a class of common regularization problems. This generalizes existing theory on normal variance mixtures for priors in regression and classification. It also allows variants of the expectation-maximization algorithm to ... |
Title: On matrix variance inequalities |
Abstract: Olkin and Shepp (2005, J. Statist. Plann. Inference, vol. 130, pp. 351--358) presented a matrix form of Chernoff's inequality for Normal and Gamma (univariate) distributions. We extend and generalize this result, proving Poincare-type and Bessel-type inequalities, for matrices of arbitrary order and for a lar... |
Title: Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise |
Abstract: The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive n... |
Title: Visual Localisation of Mobile Devices in an Indoor Environment under Network Delay Conditions |
Abstract: Current progresses in home automation and service robotic environment have highlighted the need to develop interoperability mechanisms that allow a standard communication between the two systems. During the development of the DHCompliant protocol, the problem of locating mobile devices in an indoor environmen... |
Title: Designing a Miniature Wheel Arrangement for Mobile Robot Platforms |
Abstract: In this research report details of design of a miniature wheel arrangement are presented. This miniature wheel arrangement is essentially a direction control mechanism intended for use on a mobile robot platform or base. The design is a specific one employing a stepper motor as actuator and as described can o... |
Title: Application of Threshold Techniques for Readability Improvement of Jawi Historical Manuscript Images |
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